Regulatory hotspots are genetic variations that regulate the level of expression of thousands of genes [1,2,3,4]. Finding hotspots can guide us to understand the causes and mechanism of many complex diseases and traits [5]. Thus far, genetic studies have been focused on finding regulatory hotspots using eQTL mapping. However, recent studies have reported that many of the previously identified hotspots do not replicate and that these are “spurious hotspots” induced by various unknown confounding effects such as non-biological effects during sample preparation and expression measurements. Several methods have been proposed to remove these confounding effects [6,7,8,9].
Next-generation Intersample Correlation Emended (NICE) [10] is one such method that has been reported to show superior sensitivity and specificity compared to others. In spite of its advantages, the NICE program is not widely used because of two main drawbacks. NICE is neither very user friendly nor very efficient in terms of the computing performance. It is not user friendly because prior to executing a NICE program, users are required to install several specific packages, create a script file, and perform self-debug. It is also impractical to run NICE on real datasets, as most of the eQTL datasets contain hundreds of thousands of SNPs (Single Nucleotide Polymorphisms), and hotspot analysis tools such as NICE use very complicated statistical models.
To resolve these issues, we introduce a fully automated hotspot analysis program referred to as NICE-Renew (NICER). NICER is created as a web-based tool, where users can activate an analysis by simply clicking and uploading data on the web browser. Besides, users may run NICER on Google Cloud Compute Engine or even download and install the web software as docker image to run NICER web service on their local computers or servers, in which their private data can be processed using their own resources. NICER has enhanced computing performance by incorporating multi-processing concepts in the program, for which the running time reduces linear to the number of processes used. In addition, NICER allows different data formats and provides some visualization tools, which help users better understand their result and provide a fast, more meaningful view in context.
Utilizing a yeast dataset, we show that NICER can be successfully used in an eQTL analysis to identify 29 genuine regulatory hotspots, of which 17 were previously reported elsewhere.
Implementation
In this paper, we introduce a fully automated hotspot analysis tool referred to as NICER, which provides enhanced NICE in the form of a website as well as in the form of downloadable software. The website and the documentation of the program can be accessed from the NICER website:
URL http://cblab.dongguk.edu/NICER/NICE_index.jsp
Fully-automated web-based program
A majority of NICE users are biologists, who may not have the required experience in computer science to run the NICE program. NICER is created as a web-based tool, where users can activate an analysis by simply clicking on the web browser (Fig. 1). The execution is completely hidden from the user, and the user does not need to check each stage of running the program. After the analysis has been completed, NICER automatically sends a hyperlink to the email provided by the user. From the hyperlink, users can download the result file from the server. This means that after uploading the input data to NICER, users do not have to keep their computers running while performing the analysis and that they are notified of the results via e-mail.
Google cloud compute engine
We allow users to run NICER on Google Compute Engine (GCE), which has several advantages as compared to an ordinary high-computing server. The GCE has more computational power to run programs with more threads. It is protected by Google, and suspicious activities are flagged and banned. As GCE is virtually created as a docker container, server hardware components such as CPU, RAM, and HDD can be flexibly modified, which is helpful to increase the number of parallel processes and shorten the execution time if needed. However, because the web service is uploaded to GCE, users may have to consider the sensitivity of the data and additional costs for using GCE, which they can avoid by using the free NICER on our server. For those who want to avoid the process burden of signing up to the Google Cloud service and installing NICER on it, we also provide a Google Cloud Compute Engine server link upon request.
Downloadable NICER
For those who do not want to upload their data onto either our server or the GCE, we provide NICER as a downloadable version. The downloadable NICER is provided as a web software docker image file along with the source code. Users can easily download and install the docker image to run the NICER web service on their local computer or server with a few lines of code. Users do not need to worry about any initial preparation steps for running NICE such as building the running environment, installing specific packages required to run NICE program, creating a script file to run the NICE, or preparing required input data such as t-test statistics, as they are all incorporated and implemented inside the NICER program. NICER and its detailed documentation is provided on the NICER website http://cblab.dongguk.edu/NICER/NICE_index.jsp In addition, users are allowed to alter or update the source code to run alternative tests.
Visualization tools
NICER includes graphic analysis tools to assist with interpreting analysis results. After the eQTL mapping, p-values are provided as a result. To identify the hotspots from the p-values, eQTL map is one of the most commonly used tools for the analysis. eQTL shows the strength of associations between every SNP (x-axis) and gene (y-axis) on 2-dimensional space that helps analyzing patterns of associations at a glance. A plot that shows the average of negative logarithm of p-values is another tool that can help with identifying regulatory hotspots from the p-values. NICER provides these plots as an additional service to the NICE program. Users can upload and use their own p-value tables as well. The results of the plot can be seen in a pop-up page, and the corresponding image is sent to the users’ personal e-mails for safekeeping.
Input data format
NICER allows three input formats of genotype and phenotype for the analysis. In addition to the format for original NICE method, binary PLINK [11] format and VCF, which are the most commonly used formats in Genome-Wide Association Study (GWAS) and eQTL analysis are added. Since data format varies depending on the dataset, input format compatibility in NICER saves time to preprocess the data beforehand. Users can select their data format from the tab inside the website.
Parallel processing
NICER runs on a high-performance computing server and enhances the analysis by implementing parallel processing. Within the NICE algorithm, the value of genome data is calculated in the units of SNPs and as the same algorithm is used for each unit, it could run separately for each SNP in parallel. NICER reduces the execution time by dividing the input matrix data into multiple sets and running them in parallel. As the NICE algorithm is complexly intertwined with different software for each stage, a multi-processing technique is used instead of multi-threading. Thus, the web server splits the input data into multiple sets and creates processes to run NICE, and the web engine keeps track of each process. Parallel processing shortens the execution time considerably as compared to the original NICE method.